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Abstract #3801

Motion correction of high-resolution spatiotemporally encoded MRI based on deep learning

Wei Wang1, Shuhui Cai1, Lin Chen1, Zhigang Wu2, Congbo Cai1, and Zhong Chen1
1Xiamen University, Xiamen, China, 2MSC Clinical & Technical Solutions, Philips Healthcare, Xiamen, China


In multi-shot high-resolution MRI, the motion of patients often leads to serious degradation of imaging quality. In this study, a novel motion correction method based on deep learning and spatiotemporally encoded MRI was proposed to address this problem. The proposed method is robust to motion without utilizing extra scan or parallel reconstruction. The results of simulation and in vivo rat brain experiments demonstrate its efficacy in reducing image motion artifacts when subject movement exists.

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